Quantum-Enhanced Machine Learning Model Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-Enhanced Machine Learning Model Optimization explores the potential of leveraging quantum algorithms to dramatically improve the training process of machine learning models, particularly in parameter optimization and hyperparameter selection. This paper investigates the application of quantum algorithms, specifically quantum approximate optimization algorithms (QAOA), to accelerate the training of complex models. The core claim is that utilizing quantum computing will yield substantial improvements in model performance and efficiency compared to traditional machine learning techniques. The research will detail the process of applying QAOA to a specific model type and will present results demonstrating the potential for enhanced training speed and improved model accuracy. This work offers a novel approach to optimizing machine learning models using quantum principles.

DOI: 10.5281/zenodo.22267501

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